An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot manipulator to learn and generalise motions across complex surfaces by leveraging differential mathematical operators on discrete manifolds to embed information on the geometry of the workpiece extracted from triangular meshes, and extend the Dynamic Movement Primitives (DMPs) framework to generate motions on the mesh surfaces. We also propose an effective strategy to adapt the motion to different surfaces, by introducing an isometric transformation of the learned forcing term. The resulting approach, namely MeshDMP, is evaluated both in simulation and real experiments, showing promising results in typical industrial automation tasks like car surface polishing.

A clear understanding of where humans move in a scenario, their usual paths and speeds, and where they stop, is very important for different applications, such as mobility studies in urban areas or robot navigation tasks within human-populated environments. We propose in this article, a neural architecture based on Vision Transformers (ViTs) to provide this information. This solution can arguably capture spatial correlations more effectively than Convolutional Neural Networks (CNNs). In the paper, we describe the methodology and proposed neural architecture and show the experiments' results with a standard dataset. We show that the proposed ViT architecture improves the metrics compared to a method based on a CNN.

In the context of telehealth, robotic approaches have proven a valuable solution to in-person visits in remote areas, with decreased costs for patients and infection risks. In particular, in ultrasonography, robots have the potential to reproduce the skills required to acquire high-quality images while reducing the sonographer’s physical efforts. In this paper, we address the control of the interaction of the probe with the patient’s body, a critical aspect of ensuring safe and effective ultrasonography. We introduce a novel approach based on variable impedance control, allowing the real-time optimisation of compliant controller parameters during ultrasound procedures. This optimisation is formulated as a quadratic programming problem and incorporates physical constraints derived from viscoelastic parameter estimations. Safety and passivity constraints, including an energy tank, are also integrated to minimise potential risks during human-robot interaction. The proposed method’s efficacy is demonstrated through experiments on a patient’s dummy torso, highlighting its potential for achieving safe behaviour and accurate force control during ultrasound procedures, even in cases of contact loss.

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

This paper introduces a generic filter-based state estimation framework that supports two state-decoupling strategies based on cross-covariance factorization. These strategies reduce the computational complexity and inherently support true modularity – a perquisite for handling and processing meshed range measurements among a time-varying set of devices. In order to utilize these measurements in the estimation framework, positions of newly detected stationary devices (anchors) and the pairwise biases between the ranging devices are required. In this work an autonomous calibration procedure for new anchors is presented, that utilizes range measurements from multiple tags as well as already known anchors. To improve the robustness, an outlier rejection method is introduced. After the calibration is performed, the sensor fusion framework obtains initial beliefs of the anchor positions and dictionaries of pairwise biases, in order to fuse range measurements obtained from new anchors tightly-coupled. The effectiveness of the filter and calibration framework has been validated through evaluations on a recorded dataset and real-world experiments.

Multi-robot patrolling for dynamic coverage in flat environments is proposed, through a systematic simulative analysis between the Greedy Bayesian Strategy and the Expected Reactive algorithm based on the expected idleness. The two approaches are compared against unreliable communications, communication and sensing range, and number of conflicts. In addition, we introduce a new weighting-term for the regions close to a quantity of interest detected by robots, decreasing the passing-time for those regions. Combining the proposed control strategy and a traditional distributed and recursive Weighted Least Square estimation algorithm, the swarm is capable to compute the quantity of interest position with a desired target uncertainty. Extensive simulations and comparisons are reported.

Autonomous vehicle racing is facing a growing interest both in industrial and academic settings spanning multiple disciplines. In this paper, we will explore how to create a robust, efficient, and reliable decision-making mechanism to decide, at every point in time, the trajectories that a vehicle should take to overtake its opponents and win the race. The proposed framework combines a graph-based path planner with a game-theoretic model to generate powerful racing strategies. We implemented the framework, and we carried out an experimental evaluation to show its effectiveness and evaluate the impact of the different parameters.

Rescue missions in mountain environments are hardly achievable by standard legged robots—because of the high slopes—or by flying robots—because of limited payload capacity. We present a concept for a rope-aided climbing robot which can negotiate up-to-vertical slopes and carry heavy payloads. The robot is attached to the mountain through a rope, and it is equipped with a leg to push against the mountain and initiate jumping maneuvers. Between jumps, a hoist is used to wind/unwind the rope to move vertically and affect the lateral motion. This simple (yet effective) two-fold actuation allows the system to achieve high safety and energy efficiency. Indeed, the rope prevents the robot from falling while compensating for most of its weight, drastically reducing the effort required by the leg actuator. We also present an optimal control strategy to generate point-to-point trajectories overcoming an obstacle. We achieve fast computation time ($16\ m$long jump, showing the effectiveness of the proposed approach, and confirming the interest of our concept. Finally, we performed a reachability analysis showing that the region of achievable targets is strongly affected by the friction properties of the foot-wall contact.

This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide robust and low-drift localization. In order to include range measurements in state estimation, the position of the UWB anchors must be known. This study proposes a multi-step initialization procedure to map multiple unknown anchors by an Unmanned Aerial Vehicle (UAV), in a fully autonomous fashion. To address the limitations of initializing UWB anchors via a random trajectory, this paper uses the Geometric Dilution of Precision (GDOP) as a measure of optimality in anchor position estimation, to compute a set of optimal waypoints and synthesize a trajectory that minimizes the mapping uncertainty. After the initialization is complete, the range measurements from multiple anchors, including measurement biases, are tightly integrated into the VIO system. While in range of the initialized anchors, the VIO drift in position and heading is eliminated. The effectiveness of UVIO and our initialization procedure has been validated through a series of simulations and real-world experiments.

This paper presents a parallel computing implementation of the Iterative Dynamic Programming (IDP) solution to the multipoint Markov-Dubins problem using GPUs. The multi-point Markov-Dubins problem requires the computation of the shortest path with bounded curvature that connects a sequence of planar points (waypoints). As well as being interesting in its own right, an efficient solution to this problem is key to finding optimal or suboptimal solutions of other problems such as the Dubins Travelling Salesman and the Dubins Orienteering problem. The constraint on the curvature makes the problem highly non-linear and complicates its solution. Classic methods are optimisation-based and cast the problem into the Nonlinear Programming (NLP) or Mixed Integer Nonlinear Programming (MINLP) frameworks, for which existing solutions cannot be significantly parallelised. On the contrary, the IDP solution proposed here is well suited for parallel execution. In the paper, we show that the parallel implementation of the IDP outperforms both the NLP/MINLP methods and the iterative version of the IDP methods in terms of accuracy, computation time and power consumption. Computation time and power consumption will be the main focus of the paper, because they are closely related to the implementation on an embedded platform.

The problem addressed in this paper is the localisation of a mobile robot using a combination of on-board sensors and Ultra-Wideband (UWB) beacons. Specifically, we consider a scenario in which a mobile robot travels across an area infrastructured with a small number of UWB anchors. The presence of obstacles in the environment introduces an offset in the measurements of the distance between the robot and the UWB anchors causing a degradation in the localisation performance. By using a discrete–time formulation of the system dynamics, we show that, under mild condition, the trajectories can be observed and the offset can be estimated in a finite number of steps. Besides being interesting in its on right, the global observability results offer a clear pathway towards the definition of a new generation of estimation algorithms.

This paper proposes an online optimal perception-aware strategy meant to maximize the information collected along the trajectory via the available measurements while simultaneously minimizing the negative effects of actuation/process noise. Indeed, in several robotic applications, the actuation/process noise is far from negligible and its negative effects are particularly relevant especially with intermittent measurements (e.g. collected by a vision system with limited Field-Of-View). New metrics are proposed as combinations of the Constructability Gramian, for measuring the amount of information collected via the available sensors, and the Reachability Gramian, for measuring the degrading effects of actuation/process noise. Control inputs that optimize those cost functions are provided. To show the effectiveness of our method, we consider one case study involving a unicycle-like vehicle, subject to Gaussian measurement noise and Gaussian or Brownian actuation noise, that estimates its state using intermittent distances from known environmental markers.

In this article, we present a framework for planning an activity to be executed with the support of a robotic navigation assistant. The two main components are the activity and the motion planner. The activity planner composes a sequence of abstract activities, chosen from a given set, to synthesize a plan. Each activity is associated with a point of interest in the environment and with probabilistic parameters that depend on the plan, which are characterized by simulations in realistic scenarios. The low-level action to pass from an activity to the next is handled by the motion planner, which secures the physical feasibility of the chosen actions and their compatibility with the constraints posed by the user and the environment. Indeed, the final plan must respect the user constraints and optimise his/her satisfaction from the activity. We show a possible model for the problem as a chance constrained optimization along with an efficient technique to find high-quality solutions.

We present an algorithm for collision free and socially-aware navigation of multiple robots in an environment shared with human beings, other robots and with the presence of static obstacles. We formulate the problem as a constrained optimization problem, where the cost function is chosen in order for the robotic agents to exhibit bio-inspired behaviors, such as cooperation inside the group and cohesive motion. Some of the constraints are required to avoid collision between the agents and with other obstacles and emanate from the application of Velocity Obstacle approach. The nonholonomic dynamics of the vehicles, is managed through the application of the feedback linearization technique to map the velocities in the control values. In this paper we propose both centralized solution and a completely decentralized solution. The overall strategies are extensively tested in simulations.

In the field of exploration strategies for teams of autonomous vehicles, one relevant set of solutions build upon the so called foraging algorithms, which mimic the foraging strategies of animals and insects, such as bugs and/or ant colonies. In the literature, it is most often observed that the choice of the foraging strategy to be applied for a specific swarm robotics problem does not rely on quantitative and objective selection criteria but, rather, it is guided solely by qualitative guidelines. Hence, this paper proposes a quantitative review of four popular foraging strategies, namely solitary foraging, behavioural matching, stigmergical foraging and signalling. A quantitative evaluation of their performance in terms of collectible or goal acquisition in different operating scenarios is proposed together with a comparison of their computation times when the size of the swarm changes. The comparative simulations presented to provide evidence of the different approaches efficiency have been implemented with the ARGoS simulation tool.

We present a Lloyd-based navigation solution for robots that are required to move in a dynamic environment, where static obstacles (e.g, furnitures, parked cars) and unpredicted moving obstacles (e.g., humans, other robots) have to be detected and avoided on the fly. The algorithm can be computed in real-time and falls in the category of the reactive methods. Moreover, we propose an extension to the multi-agent case that deals with cohesion and cooperation between agents. The goodness of the method is proved through extensive simulations and, for the single agent navigation in human-shared environment, also with experiments on a unicycle-like robot.

This paper addresses the problem of target detection and localisation in a limited area using multiple coordinated agents. The swarm of Unmanned Aerial Vehicles (UAVs) determines the position of the dispersion of stack effluents to a gas plume in a certain production area as fast as possible, that makes the problem challenging to model and solve, because of the time variability of the target. Three different exploration algorithms are designed and compared. Besides the exploration strategies, the paper reports a solution for quick convergence towards the actual stack position once detected by one member of the team. Both the navigation and localisation algorithms are fully distributed and based on the consensus theory. Simulations on realistic case studies are reported.

Among the manifold wireless technologies recently adopted for indoor localization and tracking, radars based on phased-array transceivers at 60 GHz are gaining momentum. The main advantages of this technology are: high accuracy, good ability to track multiple target with a low computation burden and preservation of privacy. Despite the growing commercial success of low-cost radar platforms, accurate studies to evaluate their tracking performance are not frequent in the literature, the main reasons being the commercial policies that prevent a direct access to the processed data and the difficult calibration of indoor positioning systems under dynamic conditions. This paper aims to fill this gap providing an extensive and scientifically sound performance analysis of one of these sensors (i.e., the System-onChip (SOC) TI IWR6843) and exposing benefits and limitations of 60-GHz mm-wave sensors for people localization and tracking. Multiple experimental results show that the average standard positioning uncertainty is about 30 cm under dynamic conditions. Our study also reveals the critical impact of three parameters, which are not properly documented by the manufacturer. Localization accuracy and robustness are also significantly affected by the risk of delayed and spurious detections. In the paper, a possible mitigation strategy of these anomalies is presented.

We consider the problem of accurate and high-rate self-localization for a mobile robot. We adaptively combine the speed information acquired by proprioceptive sensors with intermittent positioning samples acquired via ultra-wideband (UWB) radios. These are triggered only if and when needed to reduce the positioning uncertainty, itself modeled by a probabilistic cost function. Our formulation is agnostic w.r.t. the source of uncertainty and enables an intuitive specification of user navigation requirements along with stochastic guarantees on the system operation. Experimental results in simulation and with a real platform show that our approach i) meets these guarantees in practice ii) achieves the same accuracy of a fixed periodic sampling but with significantly higher scalability and lower energy consumption iii) is resilient to errors in UWB estimates, enabling the use of low-accuracy ranging schemes which further improve these two performance metrics.

We consider the problem of guiding a senior user along a path using a robotic walking assistant. This is a particular type of path following problem, for which most of the solutions available in the literature require an exact localisation of the robot in the environment. An accurate localisation is obtained either with a heavy infrastructure (e.g., an active sensing system deployed in the environment or deploying landmarks in known positions) or using SLAM approaches with a massive data collection. Our key observation is that the intervention of the system (and a good level of accuracy) is only required in proximity of difficult decision points, while we can rely on the user in an environment where the only possibility is just to maintain a course (e.g., a corridor). The direct implication is that we can instrument the environment with a heavy infrastructure only in certain areas. This design strategy has to be complemented by an adequate control law that shifts the authority (i.e., the control of the actuators) between the robot and the user according to the accuracy of the information available to the robot. Such a control law is exactly the contribution of this paper.

After several decades of both market and scientific interest, indoor positioning is still a hot and not completely solved topic, fostered by the advancement of technology, pervasive market penetration of mobile devices and novel communication standards. In this work, we propose a two-step model-based indoor positioning algorithm based on Bluetooth Low-Energy, a pervasive and energy efficient standard protocol. In the first (i.e. ranging) step a Kalman Filter (KF) performs the fusion of both RSSI and Time-of-Flight measurement data. Thus, we demonstrate the benefit of not relying only on RSSI, comparing ranging performed with or without the help of ToF. In the second (i.e. positioning) step, the distance estimates from multiple anchors are combined into a quadratic cost function, which is minimized to determine the coordinates of the target node in a planar reference frame. The proposed solution is tailored to reduce the computational effort and target real-time execution on an embedded platform, demonstrating a limited loss of performance. The paper presents an experimental setup and discusses meaningful results, demonstrating a robust BLE-based indoor positioning solution for embedded systems.

This paper presents a localization method exploiting a mobile robot equipped with an Ultra High Frequency Radio Frequency IDentification (UHF-RFID) reader to locate stationary tags in warehouse scenarios. The measurement method is based on the Synthetic Aperture Radar (SAR) approach and the robot trajectory knowledge is achieved through a calibrated vision-based system suitable for indoor environments. The technique capability is demonstrated through an experimental analysis employing commercial UHF-RFID hardware and a wheeled robot. Localization accuracy is evaluated on the field by using a calibrated vision system, used to both locate the robotic vehicle and the tags when detected.

Summary In recent years, series of important achievements have paved the way for the introduction of probabilistic analysis in the area of soft real‐time systems design. In this article, we present an extensible design tool, called PROSIT, which facilitates the access to this technology for a potentially large number of researchers and industrial practitioners. The tool enables the probabilistic analysis of the temporal performance of a real‐time task under fixed‐priority and resource reservations scheduling algorithms. For resource reservations, the tool also offers an automatic procedure for the synthesis of scheduling parameters that optimize a quality metric related to the probabilistic behavior of the tasks.

Assistive passive robotic walkers are naturally modelled as rear-driven bicycle with control on the front steering wheels. Standard path following algorithms used for unicycle-like robots can then be readily available, e.g. using backstepping techniques, to control the walker on desired paths. However, such an approach is usually singular in the very common situation of zero velocity, e.g. whenever the vehicle starts its motion or the user stops for any reason. The paper proposes a non-singular passive path following algorithm for an assistive robotic walker equipped with front steering wheels. The control law avoids the singularities, since it is velocity-independent, and allows the designer to specify saturation limits on the steering angles. The converging properties of the non-singular velocity-independent controller in the presence of saturation constraints are firstly formally proved and tested in simulations. Then extensive experiments performed on 14 testers are presented. These tests underline the promising performance of the proposed controller and the importance of singularities-avoidance in real-world scenarios to increase human comfort along the planned trajectory.

Indoor applications based on vehicular robotics require accurate, reliable and efficient localisation. In the absence of a GPS signal, an increasingly popular solution is based on fusing information from a dead reckoning system that utilises on-board sensors with absolute position data extracted from the environment. In the application considered in this paper, the information on absolute position is given by visual landmarks deployed on the floor of the environment considered. This solution is inexpensive and provably reliable as long as the landmarks are sufficiently dense. On the other hand, a massive presence of landmark has high deployment and maintenance costs. In this paper, we build on the knowledge of a large number of trajectories (collected from environment observation) and seek the optimal placement that guarantees a localisation accuracy better than a specified value with a minimal number of landmarks. After formulating the problem, we analyse its complexity and describe an efficient greedy placement algorithm. Finally, the proposed approach is validated in realistic use cases.

Robotic walkers are a particular class of devices used to assist users with physical or cognitive impairments in their navigation of large public spaces. In this context, a guidance mechanism is a controller that steers the user towards the desired path when he/she deviates. Passive guidance mechanisms do not directly propel the vehicle and leave the user in control of his/her walk. The inherent safety of passive guidance and the intuitive behaviour of the device make this class of solutions preferable to any other for robot assisted walking. The possible ways to obtain a passive behaviour in robotic walkers either require complex and expensive sensors or generate potentially rough (bang-bang) manoeuvres that are detrimental to the user's comfort. The contribution of this work is a path following controller that simulates passivity using a pair of active motors operating on the rear wheels of the walker. The system estimates and tracks the velocity desired by the user and the motors generate a rotational torque only when a turn toward the path is required. The proposed solution delivers high levels of comfort, does not rely on expensive hardware and preserves all the important properties of a passive mechanism.

A large class of modern real-time applications exhibits important variations in the computation time and is resilient to occasional deadline misses. In such cases, probabilistic methods, in which the probability of a deadline miss can be guaranteed and related to the scheduling design choices, can be an important tool for system design. Several techniques for probabilistic guarantees exist for the resource reservation scheduler and are based on the assumption that the process describing the application is independent and identically distributed (i.i.d.). In this paper, we consider a particular class of robotic application for which this assumption is not verified. For such applications, we have verified that the computation time is more faithfully described by a Markov model. We propose techniques based on the theory of hidden Markov models to extract the structure of the model from the observation of a number of execution traces of the application. As a second contribution, we show how to adapt probabilistic guarantees to a Markovian computation time. Our experimental results reveal a very good match between the theoretical findings and the experiments.

Indoor positioning often requires detecting and recognizing ad-hoc landmarks or anchor points with known coordinates and/or a given orientation within a given reference frame. Typically, the available kind of sensors and their detection area determine the landmark features and position. Of course, an excessive use of landmarks pose serious scalability and cost issues, whereas, on the other hand, a too-low amount of deployed landmarks may create areas where agent's position is hard to track or localization accuracy drops. In addition, often sensors are not omni-directional. In this paper, the optimal placement problem of landmarks detected by sensors with a limited detection area is addressed in the general case of wide-open, ideally unbounded, rooms. First, landmarks placement optimization is performed numerically. Then, a closed-form expression of the optimal distance between landmarks on a regular pattern is determined as a function of both the reading range and the directional properties of the sensor considered. Finally, the performances of the chosen placement strategy in more realistic indoor environments (i.e. consisting of multiple rooms with obstacles therein) are evaluated through simulations assuming, without loss of generality, that a wheeled robot equipped with a front camera adjusts its own position by detecting suitable visual landmarks.

This paper presents a control algorithm that steers a robotic walking assistant along a planned path using electromechanical brakes. The device is modeled as a Dubins' car, i.e., a wheeled vehicle that moves only forward in the plane and has a limited turning radius. In order to reduce the cost of the hardware, no force sensor is employed. This feature hampers the application of control algorithms based on a modulated braking action. A viable solution is based on the application of on/off braking action, thus forcing the vehicle to turn with a fixed turning radius. In order to avoid the annoying chattering behaviour, which is the inevitable companion of all bang-bang solutions, we propose a hybrid controller based on three discrete states that rule the application of the braking action. The resulting feedback controller secures a gentle convergence of the user toward the planned path and his/her steady progress towards the destination. This is obtained by using two independent hystereses thresholds, the first one associated with the approaching phase and the second with the following phases. The system convergence toward the path is formally proved. Simulations and experiments show the effectiveness of the proposed approach and the good level of comfort for the user.

We consider the problem of self-localisation for a mobile robot in an environment with a requested level of accuracy. The robot moves in a known environment following typical trajectories, which can be characterised in statistical terms. One of the main drivers of this paper is its application to assistive robots guiding senior or impaired users in shopping centres or in other public spaces. To localise itself the robot uses onboard sensors such as encoders and inertial platforms. The level of noise in these sensors and the lack of absolute measurements determines a steady growth of the uncertainty on its position. To alleviate the problem, we assume the presence of a number of visual markers deployed in the environment. Whenever the robot comes across one of these sensors, the uncertainty on its position is reset. In the paper, we show a methodology to minimise the number of these sensors and to select their position so that the uncertainty is never worse than a given target threshold with an assigned probability.

We show a methodology for the computation of the probability of deadline miss for a periodic real-time task scheduled by a resource reservation algorithm. We propose a modelling technique for the system that reduces the computation of such a probability to that of the steady state probability of an infinite state Discrete Time Markov Chain with a periodic structure. This structure is exploited to develop an efficient numeric solution where different accuracy/computation time trade-offs can be obtained by operating on the granularity of the model. More importantly we offer a closed form conservative bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability in one real-time application of practical interest. When this bound is used for the optimisation of the overall Quality of Service for a set of tasks sharing the CPU, it produces a good sub-optimal solution in a small amount of time.

An accurate model of human behaviour is crucial when planning robot motion in human environments. The Social Force Model (SFM) is such a model, having parameters that control both deterministic and stochastic elements. We have constructed an efficient motion planning algorithm by embedding the SFM in a control loop that determines higher level objectives and reacts to environmental changes. Low level predictive modelling is provided by the SFM fed by sensors; high level logic is provided by statistical model checking. To parametrise and improve our motion planning algorithm, we have conducted experiments to consider typical human interactions in crowded environments. We have identified a number of behavioural patterns which may be explicitly incorporated in the SFM to enhance its predictive power. In this paper we describe the results of these experiments and how we parametrise the SFM.

Models are used in control domains for early validation of system properties, using simulation or formal verification, and for the automatic generation of a software implementation. We propose an approach in which a functional model of the controls is matched to a model of the execution platform through an intermediate mapping model, that represents the software tasks and communication messages. The functional model is (partly) developed in Simulink and code is generated for each subsystem. Next, an abstract view of the functional model is imported in SysML. Using SysML, a model of the execution platform is created, and an implementation of the subsystems as a set of tasks and messages is defined and evaluated. The M2T Acceleo tool processes the mapping model and generates the Orocos-compliant task code executing the C/C++ functions generated from Simulink, and the inter-task communication. This paper outlines the proposed flow and provides the description of a robotic car testbench used to show the application of the methodology. The testbench has enough functional complexity and a distributed implementation to justify the creation of architecture models, while requiring a moderate cost and effort for its construction by the interested researchers.

We consider a set of control tasks sharing a CPU and having stochastic execution requirements. Each task is associated with a deadline: when this constraint is violated the particular execution is dropped. Different choices of the scheduling parameters correspond to a different probability of deadline violation, which can be translated into a different level for the Quality of Control experienced by the feedback loop. For a particular choice of the metric quantifying the global QoC, we show how to find the optimal choice of the scheduling parameters.

This paper presents a position tracking technique based on multisensor data fusion for rollators helping elderly people to move safely in large indoor spaces such as public buildings, shopping malls or airports. The proposed technique has been developed within the FP7 project DALi, and relies on an extended Kalman filter processing data from dead-reckoning sensors (i.e. encoders and gyroscopes), a short-range radio frequency identification (RFID) system and a front Kinect camera. As known, position tracking based on dead-reckoning sensors only is intrinsically affected by growing uncertainty. In order to keep such uncertainty within wanted boundaries, the position values are occasionally updated using a coarse-grained grid of low-cost passive RFID tags with known coordinates in a given map-based reference frame. Unfortunately, RFID tag detection does not provide any information about the orientation of the rollator. Therefore, a front camera detecting some markers on the walls is used to adjust direction. Of course, the data rate from both the RFID reader and the camera is not constant, as it depends on the actual user's trajectory and on the distance between pairs of RFID tags and pairs of markers. Therefore, the average distance between tags and markers should be properly set to achieve a good trade-off between overall deployment costs and accuracy. In the paper, the results of a simulation-based performance analysis are reported in view of implementing the proposed localization and tracking technique in a real environment.

This paper presents a new Model of Computation (MoC) for real-time tasks used in control systems. This new model, named continuous stream task model, relaxes some of the constraints imposed by the traditional hard and soft real-time task models. A key advantage of the model is the possibility to easily analyse the probabilistic evolution of the delays. This leads to an easy formalisation of necessary and sufficient conditions for the stochastic stability of the closed loop system producing considerable savings in the amount of CPU bandwidth required to stabilise the system. This fact is confirmed by an extensive set of simulations.

The application of a resource reservation scheduler to soft real -- time systems requires effective means to compute the probability of a deadline miss given a particular choice for the scheduling parameters. This is a challenging research problem, for which only numeric solutions, complex and difficult to manage, are currently available. In this paper, we adopt an analytical approach. By using an approximate and conservative model for the evolution of a periodic task scheduled through a reservation, we construct a closed form lower bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability for many real -- time applications of interest.

High performance robotics is traditionally considered as an application area reserved to university laboratories and to the research centres of a limited group of company, which can afford high investments in equipments and system engineering. We make the point that this is not necessarily true if an adequate design is used to compensate for the lack of sophisticated sensors. To prove the validity of this idea we propose a concrete case study: driving a car-like vehicle at a high speed with a cost of the hardware below 500 Euros.

In this paper, we propose two adaptive scheduling approaches to support real-time control applications with highly varying computation times. The use of a resource reservation scheduler enables the construction of a dynamic model describing the evolution of the computing delays, which can be incorporated in the system closed loop dynamics. The two approaches differ for the assumptions on the sequence of computation time. In the first approach, we have only an aggregate information (best case and worst case computation time) and design an adaptive scheduler that maintains the delay within the maximum bound compatible with the asymptotic stability of the system. In the second case, we assume a deeper knowledge on the distribution of the computation time and design an adaptive scheduler that ensures second moment stability of the system. The two approaches are evaluated on a case study exposing the different trade-offs between bandwidth and performance.

We consider the problem of predicting the probability of an accident in working environments where human operators and robotic manipulators co-operate. We show how, starting from a stochastic discrete time system describing human motion, it is possible to construct a discrete abstraction of the system (a discrete time Markov Chain) to predict the possible trajectories starting from an initial point. The DTMC is used to predict the future evolution for the system, for a fixed horizon, pinpointing the states that, at each step, can be marked as dangerous. This way, the system estimates the probability of an accident and stops the robot when the result is greater than a threshold.

In this paper, we present a methodology for designing embedded controllers based on the so-called anytime control paradigm. A control law is split into a sequence of subroutine calls, each one fulfilling a control goal and refining the result produced by the previous one. We propose a design methodology to define a feedback controller structured in accordance with this paradigm and show how a switching policy of selecting the controller subroutines can be designed that provides stability guarantees for the closed-loop system. The cornerstone of this construction is a stochastic model describing the probability of executing, in each activation of the controller, the different subroutines. We show how this model can be constructed for realistic real-time task sets and provide an experimental validation of the approach.

This paper presents a complete characterization of shortest paths to a goal position for a robot with unicycle kinematics and an on-board camera with limited field-of-view (FOV), which must keep a given feature in sight. Previous work on this subject has shown that the search for a shortest path can be limited to simple families of trajectories. In this paper, we provide a complete optimal synthesis for the problem, i.e., a language of optimal control words, and a global partition of the motion plane induced by shortest paths, such that a word in the optimal language is univocally associated with a region and completely describes the shortest path from any starting point in that region to the goal point. An efficient algorithm to determine the region in which the robot is at any time is also provided.

Mobile robots are increasingly utilised in automated plants to the purpose of moving wares and material between the different production lines and logistic areas. In this context, the presence of human operators in the facility is frequently allowed to carry out or supervise some phases of the production. The problem arises of how to make the coexistence possible with controlled risks for the operator and without affecting the productivity with frequent interruptions. In this paper we propose a solution to this problem based on a probabilistic technique. A system of visual sensor (mounted on the mobile robots) detects the presence of a human operator and a discrete abstraction (essentially a discrete-time Markov chain) is used to predict his/her motion and hence find the probability of an accidental injury. For the computation of the latter, we combine the probability of having a collision with a given speed with the probability of receiving an injury out of the collision (taken from physiological models suggested by the automotive literature).

In this paper we present a methodology for designing embedded controllers with a variable accuracy. The adopted paradigm is the so called any-time control, which derives from the computing paradigm known as "imprecise computation". The most relevant contributions of the paper are a procedure for designing an incremental control law, whose different pieces cater for increasingly aggressive control requirements, and a modelling technique for the execution platform that allows us to design provably correct switching policies for the controllers. The methodology is validated by both simulations and experimental results.

This paper presents a correct solution to the optimal visual feedback control for a nonholonomic vehicle with limited field-of-view. Previous work on this subject has shown that the search for a shortest path can be limited to simple families of trajectories. We preliminarily provide an extension of the alphabet of optimal control words, to cover some regions of the vehicle plane where the synthesis of turns out to be suboptimal. The main contribution of this paper is an algorithm to translate the optimal synthesis to the image plane, thus enabling a purely image-based optimal control scheme. This allows better performance and increases the robustness of the overall process, avoiding the need of slowly-converging and error-prone parameter estimation algorithms. Simulations and experiments are reported which demonstrate the effectiveness of the proposed technique.